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TensorFlow 2 Reinforcement Learning Cookbook

Name: TensorFlow 2 Reinforcement Learning Cookbook
Author: Palanisamy
ISBN: 9781838982546

by Palanisamy

January 2021

Intermediate to advanced

472 pages

8h 24m

English

Packt Publishing

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TensorFlow 2 Reinforcement Learning Cookbook
Why subscribe?ContributorsAbout the authorAbout the reviewerPackt is searching for authors like you
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchReviews
Chapter 1: Developing Building Blocks for Deep Reinforcement Learning Using Tensorflow 2.x
Technical requirementsBuilding an environment and reward mechanism for training RL agentsGetting readyHow to do it…How it works…Implementing neural network-based RL policies for discrete action spaces and decision-making problemsGetting readyHow to do it…How it works…Implementing neural network-based RL policies for continuous action spaces and continuous-control problemsGetting readyHow to do it…How it works…Working with OpenAI Gym for RL training environmentsGetting readyHow to do it…How it works…See alsoBuilding a neural agentGetting readyHow to do it…How it works…Building a neural evolutionary agentGetting readyHow to do it…How it works…See also
Chapter 2: Implementing Value-Based, Policy-Based, and Actor-Critic Deep RL Algorithms
Technical requirementsBuilding stochastic environments for training RL agentsGetting readyHow to do it…How it works…Building value-based reinforcement learning agent algorithmsGetting readyHow to do it…How it works…Implementing temporal difference learningGetting readyHow to do it…How it works…Building Monte Carlo prediction and control algorithms for RLGetting readyHow to do it…How it works…Implementing the SARSA algorithm and an RL agentGetting readyHow to do it…How it works…Building a Q-learning agentGetting readyHow to do it…How it works…Implementing policy gradientsGetting readyHow to do it…How it works…Implementing actor-critic RL algorithmsGetting readyHow to do it…How it works…
Chapter 3: Implementing Advanced RL Algorithms
Technical requirementsImplementing the Deep Q-Learning algorithm, DQN, and Double-DQN agentGetting readyHow to do it…How it works…Implementing the Dueling DQN agentGetting readyHow to do it…How it works…Implementing the Dueling Double DQN algorithm and DDDQN agentGetting readyHow to do it…How it works…Implementing the Deep Recurrent Q-Learning algorithm and DRQN agentGetting readyHow to do it…How it works…Implementing the Asynchronous Advantage Actor-Critic algorithm and A3C agentGetting readyHow to do it…How it works…Implementing the Proximal Policy Optimization algorithm and PPO agentGetting readyHow to do it…How it works…Implementing the Deep Deterministic Policy Gradient algorithm and DDPG agentGetting readyHow to do it…How it works…
Chapter 4: Reinforcement Learning in the Real World – Building Cryptocurrency Trading Agents
Technical requirementsBuilding a Bitcoin trading RL platform using real market dataGetting readyHow to do it…How it works…Building an Ethereum trading RL platform using price charts Getting readyHow to do it…How it works…Building an advanced cryptocurrency trading platform for RL agentsGetting readyHow to do it…How it works…Training a cryptocurrency trading bot using RLGetting readyHow to do it…How it works…
Chapter 5: Reinforcement Learning in the Real World – Building Stock/Share Trading Agents
Technical requirementsBuilding a stock market trading RL platform using real stock exchange dataGetting readyHow to do it…How it works…Building a stock market trading RL platform using price chartsGetting readyHow to do it…How it works…Building an advanced stock trading RL platform to train agents to mimic professional tradersGetting readyHow to do it…How it works…
Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos
Technical requirementsBuilding learning environments for real-world RLGetting readyHow to do it…How it works…Building an RL Agent to complete tasks on the web – Call to ActionGetting readyHow to do it…How it works…Building a visual auto-login botGetting readyHow to do it…How it works…Training an RL Agent to automate flight booking for your travelGetting readyHow to do it…How it works…Training an RL Agent to manage your emailsGetting readyHow to do it…How it works…Training an RL Agent to automate your social media account managementGetting readyHow to do it…How it works…
Chapter 7: Deploying Deep RL Agents to the Cloud
Technical requirementsImplementing the RL agent’s runtime componentsGetting readyHow to do it…How it works…Building RL environment simulators as a serviceGetting readyHow to do it…How it works…Training RL agents using a remote simulator serviceGetting readyHow to do it…How it works…Testing/evaluating RL agentsGetting readyHow to do it…How it works…Packaging RL agents for deployment – a trading botGetting readyHow to do it…How it works…Deploying RL agents to the cloud – a trading Bot-as-a-ServiceGetting readyHow to do it…How it works…
Chapter 8: Distributed Training for Accelerated Development of Deep RL Agents
Technical requirementsDistributed deep learning models using TensorFlow 2.x – Multi-GPU trainingGetting readyHow to do it...How it works...Scaling up and out – Multi-machine, multi-GPU trainingGetting readyHow to do it...How it works...Training Deep RL agents at scale – Multi-GPU PPO agentGetting readyHow to do it...How it works...Building blocks for distributed Deep Reinforcement Learning for accelerated trainingGetting readyHow to do it...How it works...Large-scale Deep RL agent training using Ray, Tune, and RLLibGetting readyHow to do it...How it works...

Chapter 9: Deploying Deep RL Agents on Multiple Platforms
Technical requirementsPackaging Deep RL agents for mobile and IoT devices using TensorFlow LiteGetting readyHow to do it...How it works...Deploying RL agents on mobile devicesGetting readyHow to do it...How it works...Packaging Deep RL agents for the web and Node.js using TensorFlow.jsGetting readyHow to do it...How it works...Deploying a Deep RL agent as a serviceGetting readyHow to do it...How it works...Packaging Deep RL agents for cross-platform deploymentGetting readyHow to do it...How it works...
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Content preview from TensorFlow 2 Reinforcement Learning Cookbook

Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos

An RL Agent needs to interact with the environment to learn and train. Training RL Agents for real-world applications usually comes with physical limitations and challenges. This is because the Agent could potentially cause damage to the real-world system it is dealing with while learning. Fortunately, there are a lot of tasks in the real world that do not necessarily have such challenges, and yet can be very useful for completing the day-to-day real-world tasks that are available in our To-Do lists!

The recipes in this chapter will help you build RL Agents that can complete tasks on the internet, ranging from responding to annoying popups, ...

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TensorFlow 2 Reinforcement Learning Cookbook

by Palanisamy

Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos

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